Brain tumor segmentation in Magnetic Resonance Imaging is an important task for neurosurgeons, oncologists, and radiologists to assess disease burden and measure tumor response to treatment. Over 237,000 individuals worldwide are estimated to have been diagnosed with malignant brain and CNS with over 174,000 deaths. In the United States alone, over 66,000 new cases of primary malignant and non-malignant brain and CNS tumors are expected to be diagnosed in 2014. Detection of brain tumors with the exact location and orientation is extremely important for effective diagnosis, treatment planning, and analysis of treatment effectiveness; however, manual delineation of the tumor takes considerable time and is prone to error and wide variability. The overall goal of this proposal is to develop a scalable and automated approach for the segmentation of brain tumors. The aims of the project are: 1) Produce a clinic ready software package with user-friendly graphical user interface to manage the process of brain tumor segmentation and quantitative imaging. 2) Implement the production software module to accurately detect and classify brain tissues from multi-channel MRI data. 3) Support quantitative imaging, system interoperability, structured reporting, and knowledge integration through the use of semantics and annotation standards. 4) Demonstrate the software produces clinically validated results for accurate assessment from MRI data of the brain under varying conditions of noise, spatial inhomogeneities, localized scanner settings and vendor equipment. 5) Package, deploy, and test the SABTS tools to be used in clinical practice for the accurate detection, visualization, and assessment of disease progression in patients with brain tumors.